{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "initial_id",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "ExecuteTime": {
     "end_time": "2023-10-31T11:07:01.158423Z",
     "start_time": "2023-10-31T11:06:57.098357600Z"
    }
   },
   "outputs": [],
   "source": [
    "import h5py\n",
    "from sklearn.svm import SVC\n",
    "import matplotlib.pyplot as plt\n",
    "from keras.models import load_model\n",
    "from yellowbrick.classifier import ROCAUC\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import classification_report\n",
    "from yellowbrick.classifier import ConfusionMatrix\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from yellowbrick.classifier import ClassificationReport\n",
    "from yellowbrick.classifier import PrecisionRecallCurve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3a773ee906c1e923",
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-10-31T11:07:01.172652600Z",
     "start_time": "2023-10-31T11:07:01.159420800Z"
    }
   },
   "outputs": [],
   "source": [
    "plt.rcParams['font.sans-serif'] = ['Times New Roman']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1aa573019c78bd72",
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-10-31T11:07:03.657552500Z",
     "start_time": "2023-10-31T11:07:01.174648300Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "17/17 [==============================] - 2s 80ms/step\n",
      "5/5 [==============================] - 0s 66ms/step\n"
     ]
    }
   ],
   "source": [
    "# 加载CNN模型\n",
    "CNNModel = load_model('Models\\\\CNN.h5')\n",
    "\n",
    "# 加载CNN数据\n",
    "with h5py.File('Models\\\\CNNData.h5', 'r') as file:\n",
    "    X_train = file['X_train'][:]\n",
    "    y_train = file['y_train'][:]\n",
    "    X_test = file['X_test'][:]\n",
    "    y_test = file['y_test'][:]\n",
    "\n",
    "# 使用CNN提取特征\n",
    "X_train_features = CNNModel.predict(X_train)\n",
    "X_test_features = CNNModel.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3eb769c545a1d898",
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-10-31T11:07:03.705447500Z",
     "start_time": "2023-10-31T11:07:03.656555700Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "SVC(C=1, gamma=2, random_state=0)",
      "text/html": "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(C=1, gamma=2, random_state=0)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(C=1, gamma=2, random_state=0)</pre></div></div></div></div></div>"
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 使用SVM分类器\n",
    "svm = SVC(kernel='rbf', random_state=0, gamma=2, C=1)\n",
    "svm.fit(X_train_features, y_train[:,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c7457e4a68bafd8",
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-10-31T11:07:03.717416100Z",
     "start_time": "2023-10-31T11:07:03.688493700Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 90.44%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.89      0.93      0.91        68\n",
      "         1.0       0.92      0.88      0.90        68\n",
      "\n",
      "    accuracy                           0.90       136\n",
      "   macro avg       0.91      0.90      0.90       136\n",
      "weighted avg       0.91      0.90      0.90       136\n"
     ]
    }
   ],
   "source": [
    "y_pred = svm.predict(X_test_features)\n",
    "print('Accuracy: %.2f%%' % (accuracy_score(y_test[:,0], y_pred) * 100))\n",
    "print(classification_report(y_test[:,0], y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9aee52f38efd3c4a",
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-10-31T11:07:04.108595300Z",
     "start_time": "2023-10-31T11:07:03.720408400Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 800x550 with 2 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 分类报告可视化\n",
    "visualizer = ClassificationReport(svm, classes=['potholes', 'normal'])\n",
    "visualizer.fit(X_train_features, y_train[:,0])\n",
    "visualizer.score(X_test_features, y_test[:,0])\n",
    "plt.title('CNN-SVC Classification Report')\n",
    "plt.savefig('Figures\\\\[CNN-SVC]分类报告.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "db70e08bafe4ebdc",
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-10-31T11:07:04.235495800Z",
     "start_time": "2023-10-31T11:07:04.111588200Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 800x550 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# yellowbrick绘制学混淆矩阵热力图\n",
    "cm = ConfusionMatrix(svm, classes=['potholes', 'normal'])\n",
    "cm.fit(X_train_features, y_train[:,0])\n",
    "cm.score(X_test_features, y_test[:,0])\n",
    "plt.title('CNN-SVC Confusion Matrix')\n",
    "plt.savefig('Figures\\\\[CNN-SVC]混淆矩阵热力图.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "40e7acef5a81af2a",
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-10-31T11:07:04.466837200Z",
     "start_time": "2023-10-31T11:07:04.236493Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 800x550 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# yellowbrick绘制ROC曲线\n",
    "visualizer = ROCAUC(svm, classes=['potholes', 'normal'], binary=True)\n",
    "visualizer.fit(X_train_features, y_train[:,0])\n",
    "visualizer.score(X_test_features, y_test[:,0])\n",
    "plt.legend()\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('False Positive Rate')\n",
    "plt.title('ROC Curves for CNN-SVC')\n",
    "plt.savefig('Figures\\\\[CNN-SVC]ROC曲线.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "83aa195116473f6d",
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-10-31T11:07:04.701387700Z",
     "start_time": "2023-10-31T11:07:04.470825600Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 800x550 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# yellowbrick绘制精度-召回率曲线\n",
    "visualizer = PrecisionRecallCurve(svm, classes=['potholes', 'normal'])\n",
    "visualizer.fit(X_train_features, y_train[:,0])\n",
    "visualizer.score(X_test_features, y_test[:,0])\n",
    "plt.legend()\n",
    "plt.xlabel('Recall')\n",
    "plt.ylabel('Precision')\n",
    "plt.title('Precision Recall Curve for CNN-SVC')\n",
    "plt.savefig('Figures\\\\[CNN-SVC]精确率召回率曲线.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8910635e79fa3269",
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-10-31T11:07:04.774521600Z",
     "start_time": "2023-10-31T11:07:04.703381300Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.96428571 0.88888889 0.88888889 0.85185185 0.92592593]\n",
      "Accuracy: 0.90 (+/- 0.08)\n"
     ]
    }
   ],
   "source": [
    "# 使用五折交叉验证\n",
    "scores = cross_val_score(svm, X_test_features, y_test[:,0], cv=5)\n",
    "# 输出交叉验证分数\n",
    "print(scores)\n",
    "print(\"Accuracy: %0.2f (+/- %0.2f)\" % (scores.mean(), scores.std() * 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [],
   "source": [
    "import pickle\n",
    "with open('Models\\\\CNN-SVM.pkl','wb') as f:\n",
    "    pickle.dump(svm, f)\n",
    "f.close()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-10-31T11:07:04.774521600Z",
     "start_time": "2023-10-31T11:07:04.733301400Z"
    }
   },
   "id": "ca1b1754b9c4d954"
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
